Cloud-based Social Application Deployment using Local Processing and Global Distribution

Transcription

1 Cloud-based Socal Applcaton Deployment usng Local Processng and Global Dstrbuton Zh Wang *, Baochun L, Lfeng Sun *, and Shqang Yang * * Bejng Key Laboratory of Networked Multmeda Department of Computer Scence and Technology, Tsnghua Unversty Department of Electrcal and Computer Engneerng, Unversty of Toronto {wangzh4@mals., sunlf@, yangshq@}tsnghua.edu.cn, bl@eecg.toronto.edu ABSTRACT Socal applcatons represent a paradgm shft on how the Internet s to be used, and have already changed the way we work, lve, and play. When t comes to deployng socal applcatons, cloud computng platforms are used to meet the Internet-scale, self-propagatng, and fast-growng demands from these applcatons. Yet, to deploy socal meda applcatons n the most effectve and economc fashon, we need to strategcally desgn and follow a set of theoretcal and practcal prncples. In ths paper, we seek to desgn a set of new prncples to gude socal applcaton deployment. Learnng from large-scale measurement-based observatons usng a real-world socal applcaton, the gst of our prncples s to detach the typcally ntegrated collecton processng dstrbuton workflows n socal applcatons nto separate local processng and global dstrbuton procedures, whch can be effectvely deployed usng dfferent cloud servces. Moreover, based on a predctve model of regonal propagaton, we formulate the resource allocaton problems n the processes of collectng/processng and dstrbutng content as two optmzaton problems, whch can be solved by effcent algorthms. Fnally, based on our theoretcal desgn, we have mplemented an example socal applcaton on Amazon EC2 and Google AppEngne, where IaaS-based computaton nstances perform content collecton and processng, and the PaaS-based platform s employed to dstrbute the contents that are wdely propagatng. Our PlanetLab-based tracedrven experments have further confrmed the superorty of our desgn. Categores and Subject Descrptors C.2.4 [Dstrbuted System]: Dstrbuted Applcatons; H.4 [Informaton Retreval]: Socal Network Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse, to republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. CoNEXT 12, D ecember 1 13, 212, Nce, France. Copyrght 212 ACM /12/12...$15.. General Terms Measurement, Desgn Keywords Socal applcaton deployment, onlne socal network, cloud computng 1. INTRODUCTION Applcatons deployed n onlne socal networks [18] have emerged as one of the most popular means for users to access multmeda contents n today s Internet [14]. Ths s due to a new development scheme n onlne socal networks: by smply becomng developers 1 of large socal networks lke Facebook, socal meda companes can use user profles and socal relatonshps va Open APIs 2, and are able to develop applcatons for mllons of potental users, wthout buldng a new socal network. At the end of March 212, over 9 mllon apps ntegrated wth Facebook are usng such a development paradgm 3. In ths paper, we focus on the problem faced by socal meda companes after new applcatons have been developed: how do we effectvely and economcally deploy these applcatons? The deployment of a socal applcaton s challengng due to a number of unque evoluton characterstcs: (1) It s potentally Internet-scale from the begnnng, snce a socal applcaton depends on an onlne socal network to drectly attract ts global users, makng the number of users grow much faster than tradtonal multmeda applcatons; (2) t s self-propagatng, snce the applcaton can be recommended to users by ther frends when they are usng the socal applcaton; and (3) t s fast-growng, snce the number of users can ncrease rapdly due to propagaton caused by the socal effects. As an example, the socal applcaton WeChat depends on Tencent Inc. s socal network servces, and has ht a record of 2 mllon users n less than 14 months 4. Snce hghly scalable and elastc network resources are requred to deploy new socal applcatons, t s promsng to deploy socal applcatons n the cloud for a number of reasons: (1) Small socal applcaton companes, whch develop API 3 =

2 socal applcatons for large socal networks, can buld ther own global servce by smply becomng customers of cloud provders; (2) the system can easly scale when the number of ts users and the volume of ts contents ncrease; and (3) socal applcatons can be easly mplemented n the cloud due to complete control of servers based on vrtualzaton (e.g., vrtual machnes (VMs) runnng dfferent operatng systems). Cloud computng has been wdely used to handle varous tradtonal multmeda contents [15, 22], e.g., Netflxhas been delverng ts moves to users based on the Amazon cloud nfrastructure snce 21 [3]. Due to ts unque propagaton patterns, efforts have been devoted n the deployment of socal meda. Wang et al. [3] observed that nformaton n an onlne socal network can be used to predct content access n a standalone content sharng system, whch can gude content deployment. Cheng et al. [9] have studed the parttonng schemes for socal contents to acheve a balanced load at the servers and preserve socal relatonshp. Wu et al. [31] have studed cost-effectve vdeo dstrbuton n a socal network by mgratng vdeos n geo-dstrbuted clouds. However, exstng studes only solve the content dstrbuton problem n socal meda; n ths paper, we study the deployment of socal applcaton, whch ncludes content collecton, processng and dstrbuton. In a typcal socal applcaton, user-generated contents (UGCs) are the domnant form of contents,.e., theyare frst generated by users, then collected and processed by the system, and fnally dstrbuted to other users through the socal relatonshps. To deploy a socal applcaton, we take the characterstcs of socal meda nto account as follows: (1) Users are the sources of contents n socal meda. Instead of central content provders, users are the ones who generate contents for socal meda [11]; (2) socal meda s dynamcally processed and aggregated,.e., contents generated by users are uploaded to the socal meda system, whch performs varous processng tothesecontents anddstrbutes the processed contents to users [19]; and (3) the dstrbuton of socal meda s severely affected by socal propagaton [29]. Propagaton wth socal meda s no longer random t s determned by the socal network topology and user sharng [8][27]. Accordng to the propertes of content collecton, processng and dstrbuton, we allocate computaton, storage and network resources from the cloud to deploy a new socal applcaton as follows. (1) Local processng contentsare ntally collected and processed by cloud servers that are geographcally close to the user generatng them. Snce content processng vares from one applcaton to another, we deploy the processng part usng IaaS (Infrastructure as a Servce)-based nstances, e.g., VM nstances provded by Amazon EC2 (Elastc Compute Cloud) [1], where the applcaton can be mplemented n varous programmng languages. However, t s costly and dffcult to buld a hghly scalable and global dstrbuton platform to serve users over the world based on IaaS only. (2) Global dstrbuton processed contents are fnally dstrbuted by servers that are geographcally close to users who receve such contents. Fortunately, cloud computng provdes another resource allocaton scheme, PaaS (Platform as a Servce), e.g., Google AppEngne [2], where resource s provded to users n an auto-scaled manner. In our desgn, we buld the dstrbuton platform usng PaaS to dstrbute the contents processed by IaaS-based computaton nstances. In our cloud-based socal applcaton deployment, we are presented wth the followng challenges: (1) How should we allocate IaaS-based computaton nstances to process contents generated by users located wthn dfferent regons? (2) How should we choose contents to be replcated to a PaaS-based dstrbuton platform to serve global users? and (3) How should we desgn effcent protocols to connect local content processng and global content dstrbuton? In ths paper, we answer these questons by connectng the characterstcs n socal meda propagaton wth ts deployment desgn. Our contrbutons can be summarzed as follows: (1) We conduct extensve measurements to study the propagaton characterstcs n socal meda and motvate our desgn; (2) We provde theoretcal gudelnes for socal applcaton deployment usng local processng and global dstrbuton; and (3) We mplement an example socal applcaton to evaluate the effectveness and effcency of our desgn based on Amazon EC2, Google AppEngne and PlanetLab. The remander of ths paper s organzed as follows. We revew related work n Sec. 2. We conduct large-scale measurements to study the characterstcs of socal meda n Sec. 3. We present our detaled desgn and analyss n Sec. 4. We dscuss our mplementatons n Sec. 5. We evaluate the performance of our desgn n Sec. 6. Fnally, we conclude the paper n Sec RELATED WORK In ths secton, We dscuss our work n lght of the exstng lterature on socal meda deployment and cloud computng, respectvely. Onlne socal applcatons. In a socal meda system, contents spread among users by users sharng them. A number of research efforts have been devoted to studyng content propagaton n socal meda applcatons. Kwak et al. [2] nvestgated the mpact of users retweets on nformaton dffuson n Twtter. Socal applcatons have greatly changed our assumptons n tradtonal content servce deployment, e.g., content dstrbuton s shfted from a central-edge manner to an edge-edge manner, resultng n the massve volume of user-generated contents and a dynamcally skewed popularty dstrbuton [7]. In ths paper, we not only focus on the dstrbuton of contents already n an onlne socal network, but also the collecton and processng and contents generated by users n a socal applcaton. In partcular, we explore the deployment of socal applcatons based on cloud computng. Socal applcaton deployment based on cloud computng. Cloud computng s a new computng paradgm n whch both hardware and software are provded to users over the Internet as servces, n the form of vrtualzed resources [12]. Dfferent cloud provders provde dfferent types of servces [26], ncludng IaaS, PaaS, SaaS (Software as a Servce), etc., based on dfferent prcng schemes [6], e.g., byactualcpu cycles n Google AppEngne [2] or by the number of VM nstances n Amazon EC2. Due to ts elastcty, cloud computng has also been wdely used by startup companes whose demands of resources grow over tme [15]. Tradtonal systems, such as the Web [22] and vdeo streamng [3], have been beng successfully deployed n the cloud. Among multple cloud provders, L et al. [21] have proposed a servce comparson methodology to compare the performance wth 32

3 dfferent cloud provders. Rehman et al. [25] have proposed a mult-crtera cloud servce selecton strategy, to determne the servce that best matches the users requrements from amongst numerous avalable servces. Chohan et al. [1] have studed the extenson of PaaS to facltate the dstrbuted executon of applcatons over vrtualzed cluster resources. In the context of socal applcatons, cloud computng has been explored for the socal meda dstrbuton. Pujol et al. [24] have nvestgated the dffcultes of scalng onlne socal network, and desgned a socal parttonng and replcaton mddleware n whch users frends can be co-located n the same server. Tran et al. [28] have studed the partton of contents n the onlne socal network by takng socal relatonshps nto consderaton. Cheng et al. [9] have studed the parttonng schemes for socal contents to acheve a balanced load at the servers and preserve the socal relatonshps. Wu et al. [31] have studed the problem of costeffectve vdeo dstrbuton n a socal network by mgratng vdeos n geo-dstrbuted clouds. Dfferent from related works, n ths paper, we study how content processng and dstrbuton are jontly performed by the cloud. Partcularly, we desgn deployment strateges for socal applcatons by takng the characterstcs n socal meda nto account, based on measurement studes of realworld onlne socal networks. 3. BACKGROUND AND MEASUREMENT STUDY In ths secton, we explore the desgn prncples of socal applcaton deployment and present the beneft of the cloudbased desgn usng real-world measurements. We frst show an example that has the general features of socal applcatons. Then, based on an extensve measurement study of real-world onlne socal networks and cloud systems, we show that contents n a socal applcaton can be processed locally and dstrbuted globally. 3.1 Framework of a General Socal Applcaton Though dfferent socal applcatons are desgned to provde users wth dfferent contents and experences, they share many common features: (1) Users contrbute contents to the applcatons; (2) contents are aggregated by varous approaches and provded to dfferent users; and (3) contents propagate through socal connectons of the onlne socal network. To study the cloud-based deployment of socal applcatons wth these features, we use an example socal applcaton n our measurements and our system desgn. We desgn our example applcaton to be as general as possble to capture most of the features n socal applcatons. Our example socal applcaton s called SICS, a socal and nterest-based content sharng system. In SICS, besdes contents shared by a user s frends as n Twtter-lke systems, other contents are also recommended to the user, whch s based on content processng, where computaton resources are requred to parse the contents and execute the processng algorthms. After content processng, SICS provdes the user a set of enrched contents wth the recommended ones. Fg. 1 llustrates the paradgm of SICS. a and b are the orgnal contents generated by user A and user B; whle a C A D Socal relaton Socal propagaton B E Socal Applcaton n Cloud Socal meda processng a A a' b a'+b' Socal meda dstrbuton C D E Orgnal content generaton Socal meda dstrbuton Fgure 1: Content generaton, propagaton and dstrbuton n a socal applcaton. and b are the enrched contents after content processng,.e., a = {a, a 1,a 2,...}, wherea k s a recommended contentbasedontheorgnalcontenta, andb = {b, b 1,b 2,...}, where b k s a recommended content based on content b. a and b are then provded to users C, D and E accordng to socal propagaton. As an example socal applcaton, SICS contans the general content generaton, processng and dstrbuton procedures. We observe that a general socal applcaton framework s smlar to a mcrobloggng system, e.g., users have socal relatonshps between them, contents are generated by users and processed by the system, and then dstrbuted to other users who are socally connected. We next study how the socal applcaton can be effectvely deployed, usng a measurement-drven approach based on the traces from Tencent Webo. 3.2 Regonal Analyss for Socal Applcaton Deployment Our measurement study s based on traces collected from the operaton team of Tencent Webo [4], whch s a mcrobloggng webste, where users can broadcast a message ncludng at most 14 characters to ther frends. Tencent Webo features several socal actvtes n the system, e.g., onlne chattng wth frends who mutually follow each other. We obtaned Webo traces from the techncal team of Tencent, contanng valuable runtme data of the system n 2 days (October 9 October 29) n 211. Each entry n the traces corresponds to one mcroblog publshed (whch wll beregardedasantemofuser-generated content), ncludng the ID of the mcroblog, the IP address and geographc regon of the publsher, the tmestamp when the mcroblog was posted, the IDs of the parent and root mcrobloggers f t s a re-post, and contents of the mcroblog. Snce we are focused on how multmeda contents should be handled n a socal applcaton, n our measurement study, we have targeted at vdeo contents whch are mported from external webstes. In partcular, we have collected more than 3, lnks from 5 popular vdeo sharng stes. We then retreve the mcroblogs whch are related to these lnks,.e., the mcroblogs ether nclude the lnks to these vdeos n the contents or they are re-shares of the ones that nclude the lnks. These lnks cover about 2 mllon mcroblogs n the tme span, whch are posted or re-shared by over 1 mllon users, from more than 1 regons n the propagaton B b' 33

4 (a) Socal groups sharng the same content. Sze of sharng socal group Content jbg8m Content 1MU2hg Content 1PLb7u Rank of ntal users (b) Sze of socal group versus the rank of user ntatng the sharng. Fgure 2: Socal groups sharng the same content ntalzed by dfferent users. (a) Socal groups ntated by the same user. Sze of sharng socal group User 52***86 User 85***13 User 44*** Rank of contents (b) Sze of the socal group versus the rank of contents. Fgure 3: Socal groups ntalzed by the same user sharng dfferent contents. (each regon s defned by Tencent as a cty-level geographcal area). In addton, we have also retreved the socal connectons of these users. In our study, how contents are used by Webo users wll gude our desgn of socal applcaton deployment Dynamcs of Users The most mportant characterstc of a socal applcaton s that contents are propagated between users through ther socal connectons. As a result, user nfluence s crtcal n socal meda applcaton deployment. In Fg. 2(a), a crcle represents a user, a drected edge represents the propagaton between two users, and the connected components (trees) are socal groups ntated by dfferent users sharng the same content. We observe that whle some users can attract a large number of frends to jon the group, many others have much lttle nfluence. Partcularly, n Fg. 2(b), each sample represents the sze of the sharng group versus the rank of the user ntatng the group. We observe that when the same content s shared by dfferent users, the sze of the sharng groups vares sgnfcantly Dynamcs of Contents Further, we also observe that socal meda sharng s hghly affected by the contents themselves. In Fg. 3(a), the trees are socal groups ntalzed by the same user sharng dfferent contents. We observe that dfferent contents can attract qute dfferent numbers of frends. In partcular, n Fg. 3(b), each sample represents the sze of the sharng group versus the rank of the content shared n the group. We have observed that when dfferent contents are ntally shared by the same user, the sze of the sharng groups vares sgnfcantly as well. The propagaton of contents n socal applcatons can be CDF Local propagatons Propagatons between regons Normalzed dstance between propagaton regons Fgure 4: Normalzed dstances between the propagaton regon pars. Number of regons nvolved Number of regons nvolved zpf Wdely-propagatng contents Rank of content Fgure 5: The number of regons nvolved n the dstrbuton of a specfc content. dynamcally affected by both users and contents. To effectvely allocate cloud resources for deployment, n our study, we analyze propagaton wthn a regonal level, n whch propagaton statstcs can be hghly stable and predctable. Next, we wll present the effcency of local processng and global dstrbuton, both of whch are carred out usng a regonal analyss approach. 3.3 Effcency of Local Processng Localty of Content Propagaton Frst, we show the propagaton localty between content generaton and dstrbuton. We defne a normalzed geographc dstance to measure two regons n our study as d j,wheredj max{d j s the real great-crcle dstance between } regon and regon j. Fg. 4 llustrates the normalzed geographc dstances between the regon where new content s generated and the regons where the content s dstrbuted to accordng to the socal connectons. We observe that the normalzed geographc dstances for most of the meda content propagatons are very small, e.g., more than 8% of the propagatons are wthn a normalzed dstance of.1. The reason s that a domnant porton of the contents generated by users wthn a regon wll be served to the users manly from the same regon, accordng to socal connectons that determne how nformaton flows n an onlne socal network [8]. Furthermore, we also observe the localty n content dstrbuton. We defne a regon nvolved n the content s generaton (dstrbuton) as a regon where the content s generated from (to be dstrbuted to). Fg. 5 llustrates the number of regons nvolved n the dstrbuton of a specfc content n one day. Contents are ranked n a descendng order wth respect to the number of regons nvolved n ther dstrbuton. Each sample represents the number of regons nvolved n the dstrbuton versus the rank of the content. We observe that for most of the contents, only a few regons are nvolved n ther dstrbutons. The reason s that most of the contents are dstrbuted locally,.e., many users are located wthn the same regon. The observatons ndcate that n socal applcaton deployment, computaton nstances could be allocated at multple regons so as to collect, process and dstrbute the contents locally, reducng nter-regon traffc to delver the contents across regons Stablty of Regons Involved Over Tme Due to the localty of propagaton, the socal applcaton system wll allocate computaton nstances wthn dfferent 34

5 regons to process the contents locally. However, how many and whch regons should be selected to deploy the computaton nstances s stll a queston. To answer ths queston, we next study whch regons are nvolved n content propagaton. Fg. 6(a) llustrates the number of contents generated by users from all the regons over tme. Each sample represents the number of contents generated by users n a tme slot (hour) versus the tme. We observe obvous daly pattern much more contents are generated durng the peak hours (about 7 per hour) than durng the off-peak hours (about 5 per hour). We then study the regons nvolved n content generaton. Comparng to the number of generated contents, no evdent daly pattern s observed n the number of regons nvolved n the generaton, as llustrated n Fg. 6(b). Each sample n Fg. 6(b) represents the number of regons nvolved n content generaton versus the tme. We observe that the number of regons nvolved n content generaton remans at a relatvely hgh level over tme (wth the largest number 37 and the smallest number 33 per hour). Smlar results are also observed n content dstrbuton. Fg. 6(c) llustrates the number of regons nvolved n content dstrbuton over tme. We observe that the number of regons nvolved n content dstrbuton also stays at a stable level. These observatons ndcate that contents are always generated from and to be dstrbuted to almost all the regons (the total number of regons s 41 n our traces) Predctablty of Regonal Propagaton Accordng to the observatons above, we need to allocate computaton nstances at almost all the regons avalable to collect and process the contents locally. We next nvestgate how much network and computaton resources wthn each regon we should allocate, by studyng the number of contents generated by users at each regon. Let the content generaton rate of a regon denote the number of contents generated by users wthn the regon n a gven tme slot. In Fg. 7, regons are ranked accordng to ther content generaton rates. Each sample n ths fgure represents the content generaton rate of a regon versus the rank of the regon. The popularty dstrbuton of regons s not even some regons can generate much more contents than other regons. Fg. 8 llustrates the dstrbuton of the content generaton rates at 4 dfferent regons randomly selected (n Fg. 8, the content generaton rates at level x are n [4x, 4(x+1))). We observe that the dstrbutons of content generaton rates at dfferent regons are also qute dfferent. However, n Fg. 9 whch llustrates content generaton rate of each regon over tme, we observe strong evdences of daly patterns for all regons. Ths observaton ndcates that the content generaton rate of a regon s hghly predcable. Based on the observatons, we are able to desgn a predctve model to estmate the regonal content generaton rate, whch wll be utlzed n local content processng. We wll dscuss our detaled desgn n Sec Effcency of Global Dstrbuton To handle the socal contents generated at dfferent regons locally, we allocate computaton nstances at dfferent regons to collect, process and dstrbute the contents locally. In socal meda, some contents can be very popular wth many requestng users. Such contents are referred to as wdely-propagatng contents, whch can attract users from a large number of regons, as llustrated n Fg. 5. In the dstrbuton of a wdely-propagatng content, a large fracton of users wll experence low download performance f they all download the content from the computaton nstance where the content s orgnally collected and processed, snce these users can be located at other regons far away from the orgnal regon, resultng n a low download bandwdth. To address ths problem, a global dstrbuton platform whch can effectvely dstrbute wdely-propagatng contents to users wthn many regons s needed. Due to dynamc socal propagaton, t s not always easy to predct the popularty of socal meda contents, whch s affected by not only the socal network topology but also the nfluence and preference of users. In our measurements, we wll show that contents processed by computaton nstances can be effectvely replcated to a global dstrbuton platform, whch s able to sgnfcantly mprove the dstrbuton performance. We mplement the computaton nstances n C++ on Amazon EC2 mcro nodes and the dstrbuton platform n Python on Google AppEngne. We choose the followng dfferent szes for contents that can be generated by users: 1.1 MB, 16 KB and 5 KB. We allocate computaton nstances n the 7 regons provded by EC2: Vrgna (US East), Oregon (US West), Calforna (US West), Ireland (EU West), Sngapore (Asa Pacfc), Tokyo (Asa Pacfc) and Sao Paulo (South Amerca). Meanwhle, 57 PlanetLab nodes are mplemented to upload and download contents as well, smulatng the socal applcaton users. The detaled mplementaton s to be dscussed n Sec Connectvty Between Local Processng and Global Dstrbuton When usng the dstrbuton platform to delver the wdelypropagatng contents, these contents have to be frst replcated from the computaton nstances (EC2) to the dstrbuton platform (GAE). We measure the overhead for such replcaton. As llustrated n Fg. 11, we compare the tmes computaton nstances at dfferent regons spend on uploadng the contents to the dstrbuton platform, wth the average tme that the nstances spend on drectly uploadng them to the PlanetLab nodes at dfferent locatons, n the case that the dstrbuton platform s not employed. We observe that the tme computaton nstances spend on uploadng the processed contents to the dstrbuton platform s much smaller than the average tme computaton nstances spend on uploadng the contents to the users drectly. Ths observaton ndcates that the replcaton overhead s small, compared to the tme the nstances spend on uploadng the contents to the users drectly Benefts of a Global Dstrbuton Platform Next, we show that the GAE-based dstrbuton platform outperforms the EC2-based computaton nstances n dstrbutng wdely-propagatng contents to users at multple regons. Fg. 12 compares the download tmes acheved by the computaton nstances and by the dstrbuton platform. In ths fgure, each sample represents the average tme that a PlanetLab node takes to download the content from the computaton nstance or the dstrbuton platform. We observe that for most of the PlanetLab nodes, ther download 35

6 Number of content tems generated Tme (hour) (a) The number of contents uploaded by users over tme. Number of regons nvolved n generaton Tme (hour) (b) The number of regons nvolved n content generaton over tme. Fgure 6: Socal meda generaton and dstrbuton. Number of regons nvolved n dstrbuton Tme (hour) (c) The number of regons nvolved n content dstrbuton over tme. Number of contents generated (y) x 15 (x, y) log Rank of regon (x) Fgure 7: The number of contents generated wthn a regon versus the rank of the regon. Fracton of contents n a group Group wth sze Tme slot (hour) Number of contents generated Tme slot (hour) Real Predcton Number of contents generated Fgure 8: Dstrbuton of content generaton rate at four regons. Fgure 9: The content generaton rate over tme at four regons. Fgure 1: Predcton of content generaton rates. Upload tme (sec) MB 16KB 5KB Sngapore Tokyo Calforna Ireland Vrgna Sao Paulo Oregon PlanetLab EC2 Regons or PlanetLab Fgure 11: Delvery tme comparson between the computaton nstances to the dstrbuton platform and the computaton nstances to users. tmes at EC2 are much larger than that at GAE. The reason s that GAE has already automatcally replcated the contents to dfferent locatons, so that users can be redrected to servers that can best serve them. Though t s not the focus of ths paper, nterested readers are referred to the related works devoted to the dstrbuton of socal meda contents [29][31]. The observaton ndcates that n socal meda dstrbuton, a global dstrbuton platform s needed when new content s supposed to attract users from many dfferent locatons. Motvated by ths observaton, we desgn a hybrd replcaton strategy to dstrbute the contents based on both local computaton nstances and the global dstrbuton platform, where contents attractng more users from multple regons wll be replcated to the dstrbuton platform. 4. DETAILED DESIGN OF THE SOCIAL AP- PLICATION DEPLOYMENT IN CLOUD In our measurement study, we show that a socal applcaton can be effectvely deployed based on local processng nstances and a global dstrbuton platform. In ths secton, we frst present a new framework for socal applcaton deployment, and then descrbe our detaled desgn on how to collect contents generated by users, process them and dstrbute the processed contents to the users. 4.1 Framework In a socal applcaton, though users are globally dstrbuted wthn dfferent regons when they generate contents for and download the contents from the system, content propagaton s hghly localzed. To effectvely handle the contents n a socal applcaton, we desgn a new framework as llustrated n Fg. 13: (1) IaaS-based computaton nstances are allocated to collect the contents generated by users wthn dfferent regons and perform the content processng locally; and (2) a PaaS-based dstrbuton platform s allocated to assst the dstrbuton of wdely-propagatng contents. We next demonstrate the advantages of our new framework. Orgnal contents Contents generated by user 1 Contents generated by user 2 Contents generated by user 3... Computaton nstances n Cloud Computaton nstance at regon 1 Computaton nstance at regon 2 Computaton nstance at regon 3 IaaS Dstrbuton platform n Cloud PaaS Fgure 13: Framework of the socal applcaton deployment. Local collecton and processng. In content processng, allocatng computaton nstances at multple regons has the followng advantages: (1) Allocatng computaton nstances close to users can mprove the performance for them to upload and download the contents; (2) we observe that the propagaton s hghly localzed n our measurements,.e., 36

7 Download tme (sec) GAE EC2 Download tme (sec) GAE EC2 Download tme (sec) GAE EC Rank of PlanetLab node (a) fle sze 1.1 MB Rank of PlanetLab node (b) fle sze 16 KB Rank of PlanetLab node (c) fle sze 55 KB. Fgure 12: Comparson of download tmes users experence when downloadng contents from the computaton nstances and the dstrbuton platform. contents generated wthn one regon are lkely to be requested by users wthn the same regon. Deployng computaton nstances close to users can reduce the nter-regon transmsson cost [17]; and (3) the prces for computaton nstances at dfferent locatons can be dfferent [6] t s ntrgung to nvestgate how to allocate cloud resource from dfferent locatons so that the generated contents can be effcently processed wth mnmum costs. Global dstrbuton. After beng processed by computaton nstances, users can drectly download these contents from the nstances. In our measurements, we observe that the PaaS-based dstrbuton platform can acheve much smaller download tmes for users to obtan some popular contents, whch are requested by the users at many regons. In our deployment desgn, we strategcally select a set of such wdelypropagatng contents over tme, and replcate these contents from the local computaton nstances to the global dstrbuton platform. The socal applcaton system can dramatcally mprove the dstrbuton performance when the dstrbuton platform upload to users that are far away from the orgnal computaton nstances. Due to the large number of contents generated and the lmted budget for dstrbuton, we need to strategcally determne whch contents should be replcated to the dstrbuton platform and whch ones are only served by the local computaton nstances. Table 1 summarzes mportant notatons for ease of reference. 4.2 Computaton Instance Allocaton for Local Processng Gven that computaton nstances allocated n multple regons can beneft socal applcaton deployment, the problem s to determne the allocaton strategy,.e., the capactes of computaton nstances at dfferent regons Allocaton Scheme and Cost A socal meda company has to perform nstance allocaton accordng to ts budgetary constrants. In IaaS, the general prcng rules are as follows: (1) The more nstances the socal applcaton allocates, the more the cloud provder wll charge; (2) The prces vary wth dfferent regons, e.g., the unt prce of a VM nstance n US West s hgher than the prce n US East n May 212; and (3) The prces also vary over tme. The cost s determned by nstance allocaton. In our desgn, we let vector μ = {μ 1,μ 2,...} denote the cloud nstance allocaton scheme. Each entry μ j,j R C n μ determnes the aggregate content processng rate of the cloud R C R U P H P U P S λ λ j Λ j μ j M c( μ) D (T ) S (T ) M d (S) Table 1: Important notatons Set of regons havng computaton nstances Set of regons where users are located n Unt prce for content processng rate of computaton nstance Unt prce for upload capacty of dstrbuton platform Unt prce for storage of dstrbuton platform Content generaton rate from regon Content generaton rate redrected from regon to j Content generaton rate redrected to regon j Content processng rate allocated at regon j Processng cost under allocaton scheme μ Set of canddate contents for replcaton Set of processed contents served by the global dstrbuton platform Dstrbuton cost for the contents n S nstances allocated at regon j, andr C denotes the set of regons where the computaton nstances can be allocated,.e., the cloud provder has deployed servers n data centers wthn these regons. Larger μ j ndcates that more contents can be processed n regon j per tme unt, resultng n a hgher cost. In our desgn, the cost of an allocaton scheme μ can be estmated as follows: M c( μ) = Pj H μ j, j R C where P H j s the unt prce of processng rate at regon j. Note that we assume proportonal upload and download bandwdths are also allocated at regon j accordng to μ j,so that the generated contents can be collected and dstrbuted by the computaton nstances. The prces for bandwdths are ncluded n P H j Predcton of the Content Generaton Rate n Each Regon To effcently allocate the computaton nstances wthn aregon,.e., to determne the content processng rate, we can refer to the content generaton rate n that regon. The ratonale s that t would be a waste f the content processng rate s much larger than the content generaton rate; 37

8 (T 1) (T 2) (T M) whle t takes too long for content to be processed when the content processng rate s much smaller than the content generaton rate. The effcent content processng rates depend on the actual content generaton rates. Accordng to our measurements, the regonal content generaton rate s hghly predctable. Let λ (T ), R U denote the content generaton rate at regon n tme slot T,whereR U s the set of all regons that users are located at (generally, R C R U ). In Sec. 3, we observe that the content generaton rate at each regon shows strong evdence of the daly pattern. It ndcates that the content generaton rates can be predcted usng autoregressve models [5]. In our desgn, we predct λ (T ) based on the hstorcal content generaton rates {λ,λ,...,λ }, where M s the number of prevous generaton rates to refer to n the predcton. An ARIMA (AutoRegressve Integrated Movng Average) [23] model s used,.e., (1 p Φ k L k )Y (T ) =(1+ k=1 q Θ k L k )ε (T ), k=1 where p s the order of autoregressve and q s the order of movng average. Φ k and Θ k are the parameters of the autoregressve and movng average parts, respectvely. ε (T ) s the whte nose for the statonary dstrbuton. Y (T ) s defned as follows, Y (T ) =(1 L) d λ (T ), where L s the lag operaton,.e., L d λ (T ) (T d) = λ. In our desgn, the content generaton rate at each regon (λ (T ) )s recorded hourly. To capture the daly pattern, we choose the perod of d =24hours,sothatY (T ) canberegardedaswdesense statonary. In our experments, 48 hours of hstorcal, by tranng the predctve parameters usng a maxmum lkelhood estmaton. Based on the mplementaton of ARIMA n R wth parameters p = 48 and q =, we present the predcton results of the four randomly chosen regons used n our measurement n Fg. 1. We observe that the predctve model only needs a small learnng wndow to gve an relatvely accurate estmate of the content generaton rate. records are utlzed to predct λ (T ) Computaton Instance Allocaton In the computaton nstance allocaton, we regard each content generated from a regon n R U as a task for the computaton nstances to process. After a content s uploaded by a user to a computaton nstance, t s queued to be processed at the computaton nstance. A content wll be avalable to users only after t has been processed. Our objectve s to allocate computaton nstances strategcally to mnmze the average tme for a content to be avalable to users. Fg. 14 llustrates the procedure of the computaton nstance allocaton: (1) Hstorcal content generaton rates at dfferent regons n R U are collected; (2) The current content generaton rates are estmated usng the predctve model; (3) The predcted content generaton rates are scheduled to dfferent computaton nstances wthn regons n R C ; and (4) Accordng to the scheduled content generaton rates, content processng rates are allocated. We wll provde more detals next. Predcton and schedule of generated contents. For sm- Predctve Model λ Regon 1 Regon 2... Regon M R U λ j Regon 1 Regon 2... Regon N R C Λ j Allocaton Allocaton Allocaton μ 1 μ 2... μ N Fgure 14: The allocaton of computaton nstances. plcty, we use λ to denote the predcted content generaton rate from regon n tme slot T,andλ j to denote the rate of contents generated at regon to be scheduled to the computaton nstances at regon j n tme slot T. The schedule s as follows: { λ, j =argmn k d k, λ j =, j argmn k d k,, RU,j R C. The ratonale s that we assgn a user to the regon that s closest to hm, so that he can spend the mnmum amount of tme on uploadng the generated contents to the system. Let Λ j denote the rate of contents from all regons to be uploaded to nstances allocated at regon j,.e., Λ j = R U λj,j RC. Allocaton of processng rates. Let W c( μ) denotetheaverage watng tme for the orgnal contents to be processed usng the allocaton scheme μ. Accordng to the queung model [13], we have the average watng tme for a content to be processed by the socal applcaton system as follows: Λ j j R W c( μ) = C (μ j Λ j ). j R Λ C j To make the processed contents avalable to users as soon as possble, μ s regarded as an optmzaton varable to mnmze W c( μ). We model the computaton nstance allocaton as the followng optmzaton problem: subject to: mn W c( μ), (1) μ μ j Λ j,j R C, M c( μ) B C, where B C s the budget for the allocaton. We let μ j Λ j,j R C so that the watng tme for a content to be processed s lmted. μ j Λ j,j R C ndcates that we always allocate enough nstances for the estmated volumes of contents generated by users,.e., M c({λ 1, Λ 2,...}) B C.The optmzaton s a convex programmng, whch can be effcently solved by a general water-fllng lke algorthm: we teratvely allocate a small amount of resources to the computaton nstances wthn regon k wth the largest margnal tme deducton, as follows: W c Λ j k =arg mn =arg mn j R C μ j j R j R C Λ j(μ C j Λ, j) 2 untl the budget s used up. In Sec. 5, we wll present how the algorthms are mplemented to allocate computaton nstances dynamcally. 38

9 4.3 Replcaton for Global Dstrbuton In the PaaS-based dstrbuton platform, snce both storage and upload capacty are charged accordng to the usage, our desgn s to determne whch contents to be replcated to the dstrbuton platform. When choosng processed contents to be replcated to the dstrbuton platform, we select the wdely-propagatng contents that wll be requested by users from many external regons, and let the computaton nstances serve other contents that are mostly requested by local users. The selecton s based on not only the populartes of the contents, but also the socal connectons of the users generatng these contents. Let S (T ) = {c 1,c 2,...,c S} denote the set of processed contents served by the dstrbuton platform n the tme slot T,.e., users can download contents n S (T ) n tme slot T. The dstrbuton replcaton s then to determne the contents n S (T ). In the content replcaton, there s also a budget B D for the allocaton of the dstrbuton platform. Let M d (S (T ) ) denote the cost when contents n S (T ) are served by the dstrbuton platform. The cost ncludes both the storage and upload bandwdths, whch can be formulated as follows: M d (S (T ) )=P S A(c)+P U N(c), c S (T ) c S (T ) where P S s the unt storage prce, P U s the unt upload prce, A(c) s the sze of the content c, and N(c) s the amount of bytes to be uploaded to the users that are downloadng the content from the dstrbuton platform. N(c) can be estmated as follows: N(c) =v c F uc,, R U {R(c)} where u c s the user who generate content c, F uc, s the set of user u c s frends that are located at regon, R(c) s the regon where content c s orgnally processed and served, and v c s the average number of bytes served for the content. v c can be estmated by an emprcal value αa(c), where α s the average fracton of a vdeo that users usually download [16]. The ratonale of N(c) s that the dstrbuton platform wll be n charge of uploadng the content to u c s frends that are located at external regons to reduce the download tmes. The cloud dstrbuton platform automatcally replcates contents n S (T ) to dfferent locatons so that users can be better served. We desgn a content replcaton ndex r(c) as follows: r(c) = F uc, d,r(c), R U {R(c)} where d,r(c) s the geographc dstance between regon and regon R(c). Larger r(c) ndcates that content c wll be requested by more users from more external regons, and c should be replcated to the dstrbuton platform for these users to download. In our dstrbuton platform allocaton, we determne whch contents to be replcated to the dstrbuton platform by solvng the followng problem: max r(c), (2) S (T ) c S (T ) subject to: M d (S (T ) ) B D, S (T ) D (T ), where D (T ) s the set of canddate contents that can be downloaded by users n the future. In a socal applcaton, snce users manly request the contents recently generated by users, D (T ) can be formed from the contents recently processed by the computaton nstances. The ratonale of Eq. (2) s that we select the contents that can attract more users from more external regons. Such contents cannot be well served by only local computaton nstances. By replcatng these contents to the dstrbuton platform, whch automatcally replcates them to servers close to users, better download performance can be acheved for users that are not located closely to the orgnal computaton nstances. The optmzaton can be heurstcally solved by a dynamc programmng algorthm n Sec. 5. Next, we wll dscuss the mplementaton of the cloudbased socal applcaton deployment. 5. DISCUSSION OF SYSTEM IMPLEMEN- TATION In ths secton, we dscuss the detals of our mplementaton. Our mplementaton s based on Amazon EC2 and Google AppEngne. 5.1 Computaton Instance Allocaton We allocate the computaton nstances on Amazon EC2. The computaton nstance allocaton algorthm s llustrated n Algorthm 1. Due to the lmted number of regons where the computaton nstances can be allocated, the algorthm s carred out n a centralzed manner perodcally. Frst, we collect the recent content generaton rates from all the regons n R U, whch are used to predct the current content generaton rates λ, R U. We assume the unt prces Pj H,j R C are also provded by the cloud provder. By solvng the convex optmzaton problem n Eq. (1), we have the content processng rates μ j,j R C. Accordng to the content processng rates, we allocate nstances from EC2 the processng rates determne the number and the model of the computaton nstances. Second, the computaton nstances wll receve and process the contents generated by users. At each regon, a prorty queue s utlzed to store the contents uploaded by users. The contents are prortzed to be processed by the computaton nstances as follows: (1) Contents posted n the same regon wth the computaton nstance wll be prortzed, and (2) contents are processed accordng to the tmestamps they are uploaded. 5.2 Content Replcaton Accordng to our desgn llustrated n Sec. 4.3, the optmzaton can be solved usng the dynamc programmng algorthm, assumng that both the dstrbuton prce and budget can be regarded as postve ntegers. The replcaton procedure s llustrated n Algorthm 2. Contents n set D (T ) are the recently processed ones collected from all the computaton nstances, M d ( ) s the prce functon, and r( ) s the replcaton ndex functon. We assume contents n D (T ) can be ndexed from 1 to D (T ). Let r(, j) denote 39

10 Algorthm 1 Allocaton of computaton nstances. 1: procedure Allocaton(λ (t), R U,t = T 1,T 2,...,T M, Pj H,j R C ) 2: predct λ, R U usng the predctve model 3: μ j Λ j,j R C 4: whle M c( μ) B C do 5: k arg mn j R C Wc μ j 6: μ k μ k +Δ 7: end whle 8: allocate nstances accordng to processng rates μ j,j R C 9: end procedure Algorthm 2 Replcaton of processed contents. 1: procedure Replcaton(D (T ), M d ( ), r( )) 2: for d from to B D do 3: S(,d) Φ 4: r(,d) 5: end for 6: for from 1 to D (T ) do 7: for j from to B D do 8: f j M d ({c }) and r( 1,j) <r( 1,j M d ({c })) then 9: S(, j) S( 1,j) {c } 1: r(, j) r( 1,j M d ({c })) + r(c ) 11: else 12: S(, j) S( 1,j) 13: r(, j) r( 1,j) 14: end f 15: end for 16: end for 17: S (T ) = S( D (T ),B D ) 18: end procedure the optmzed replcaton gan of deployng the canddate contents ndexed 1 to under the budget j, S(, j) denote the contents selected for replcaton. Usng the dynamcal programmng algorthm, S(, j) andr(, j) are teratvely updated, and the soluton to our replcaton problem n (2) s then S (T ) = S( D (T ),B D ). After replcaton, for a content n S (T ),usersareableto download t from ether the computaton nstance where t s processed, or the dstrbuton platform, to acheve the best download rate. Stale contents n the dstrbuton platform are removed to make room for new ones n an LFU manner. After a content s removed from the dstrbuton platform, users can stll download t from the computaton nstance. 6. EXPERIMENTAL RESULTS In ths secton, we evaluate the performance of our desgn based on a prototype of the example socal applcaton mplemented on Amazon EC2 and Google AppEngne. 6.1 Experment Setup Socal applcaton system. Amazon EC2 provdes computaton nstances at 7 regons gven n Sec. 3. We have launched one mcro VM nstance 5 at each regon, where we mplement the content collecton and processng mod- 5 ules usng C++. Snce we are usng the free-ter mcro VM nodes whch have low computaton capactes, to evaluate dfferent content processng rates n Algorthm 1, the content processng n the prototype s smplfed so that the number of processed contents s drectly determned by the processng rates wthout actual computng load. The mplementaton can be easly replaced by other content processng algorthms for dfferent socal applcatons. The prces for Amazon EC2 mcro nstances are followng the latest prces provded on the webste 6. For the content dstrbuton, we mplemented a Python-based dstrbuton platform on Google AppEngne wth 5GB storage capacty and 1GB outbound bandwdth per hour. In our experments, the dstrbuton budget B D s determned by the storage and bandwdth lmtatons,.e., we replcate contents to the dstrbuton platform under the storage and bandwdth capactes. The dstrbuton uses the data storage APIs provded by Google AppEngne to accept contents uploaded from the computaton nstances and serve users. Users. We employ 57 PlanetLab nodes to upload and download contents accordng the traces from Tencent Webo as follows: (1) In each round of the experments, a set of 41 PlanetLab nodes are randomly selected and mapped to the 41 regons n R U ; (2) The content generaton rate of each PlanetLab node s determned by the traces, as used n our measurement n Sec ; and (3) After the contents are processed by the socal applcaton system, the nodes smulate to download the processed contents: followers of the users who have generated the contents wll download the processed contents. The ratonale s that t s hghly possble for these followers to download the contents, and we use them to estmate the actual downloaders, though the number of total followers can be larger than the number of users who actually download the contents n real systems (e.g., some users are never onlne to receve the contents). Protocols. We present the practcal protocols used to connect the users and the socal applcaton system. Contents are transferred as follows. (1) A user can upload a content to one of hs local computaton nstances over TCP usng prvate protocol; (2) If a processed content should be served by the dstrbuton platform, the computaton nstance requests an uploadng URL from the dstrbuton platform, whch s generated by the data storage API provded by Google AppEngne; (3) Usng the upload URL, the content nstance can upload the processed content by postng t to the gven URL over HTTP; and (4) When downloadng contents, a user s frst provded wth a XML fle ndcatng where the contents can be downloaded,.e., ether from a computaton nstance or the dstrbuton platform. The user then downloads these contents from the computaton nstances or the dstrbuton platform. Records. In our experments, we smulate two dfferent content szes for users to upload and download: 1.1 MBand 16 KB. Each PlanetLab node wll record the tme spent on uploadng and downloadng the contents. At each Amazon EC2 nstance, we also mplement the nstance to record the tme spent on processng each content. Based on these records, we evaluate the performance of content collecton, processng and dstrbuton n terms of the tme spent on each task. 6 May, 212: Calforna.25, Vrgna.2, Oregon.2, Sngapore.25, Ireland.25, Tokyo.27 and Sao Paulo.27 (USD per hour) 31

11 Generated content upload tme (sec) Fle sze: 1.1MB Our nstance allocaton Random nstance allocaton Rank of PlanetLab nodes (a) fle sze 1.1 MB. Fgure 15: collecton. Generated content upload tme (sec) Fle sze: 16KB Our nstance allocaton Random nstance allocaton Rank of PlanetLab nodes (b) fle sze 16 KB. Comparson of upload tme n content Content processng tme Our nstance allocaton Random nstance allocaton Tme Content processng tme Our nstance allocaton Random nstance allocaton Budget (x) (a) Processng tme over tme. (b) Processng tme versus budget. Fgure 16: Comparson of processng tme n content processng. 6.2 Performance Evaluaton Content collecton. Frst, we evaluate the performance for users to upload the generated contents to the socal applcaton system. We compare our nstance allocaton wth a random nstance allocaton scheme, where processng rates at the nstances are allocated randomly. The budget for both strateges s the same, 1.2 j R C P j H Λ j. The ratonale s that the socal meda company spends 2% more than the estmated demand on the allocaton. We collect the average tme each PlanetLab node spends on uploadng the contents to the computaton nstances. Fg. 15 compares the upload tmes n the two allocaton schemes. Each sample represents the average upload tme at a node versus the rank of the node. We observe that t s much faster for users to upload contents n our desgn than n the random scheme n our desgn, about 2/3 of the nodes can upload the contents wth sze 1 MB n less than 1 second, whle most of the nodes have to spend more than 5 seconds to upload the same contents n the random scheme. The reason s that by takng the regonal content generaton rates nto consderaton, bandwdths can be allocated effcently at the computaton nstances to satsfy users uploadng requests. Content processng. Next, we evaluate the performance of content processng, n terms of the average processng tme, whch s defned as the average delay for a content to be avalable to users after t has been uploaded to the system. Fg. 16(a) compares our nstance allocaton wth the random scheme over tme, under the same budget of 1.2 j R P H C j Λ j. The contents to be processed at a computaton nstance are the contents uploaded by users. We observe that the processng tme n the random scheme s about 1 tmes larger than that n our desgn. The reason s that many contents have to wat a long tme to be processed when beng queued at a computaton nstance wth a small processng rate n the random scheme. We also observe that n our desgn, the processng tme s correlated wth the content generaton rate,.e., t takes longer for a content to be processed durng the peak hours; whle n the random scheme, the processng tmes are randomly dstrbuted. We next nvestgate the content processng performance under dfferent budgets. Fg. 16(b) llustrates the processng tme versus the budget x j R P H C j Λ j.weobservethat the processng tme decreases when the budget s ncreased n both algorthms, snce more computaton resource s allocated for the generated contents; however, the processng tme s much smaller n our desgn than n the random scheme when the budget s small (e.g., x<1.5), ndcatng that our desgn can beneft the socal meda companes when they have a lmted budget. Both algorthms can acheve Generated content download tme (sec) Fle sze: 1.1MB Our dstrbuton No GAE Popularty based dstrbuton Rank of PlanetLab nodes Rank of PlanetLab nodes (a) fle sze 1.1 MB. (b) fle sze 16 KB. Fgure 17: Comparson of download tme n content dstrbuton. Generated content download tme (sec) Fle sze: 16KB Our dstrbuton No GAE Popularty based dstrbuton a small processng tme when enough resource s allocated (e.g., x>1.8). Content dstrbuton. We also evaluate the performance for the dstrbuton of the processed contents. Smlarly, the PlanetLab nodes record the tmes spent on downloadng the contents from both the computaton nstances and the dstrbuton platform. We compare our desgn wth two smple schemes: (1) No GAE strategy n whch users only download the contents from the orgnal computaton nstances; and (2) Popularty-based dstrbuton where contents n D (T ) are replcated from the computaton nstances to the dstrbuton platform accordng to only the populartes of the users who generate the contents,.e., a content s more lkely to be replcated to the dstrbuton platform f the user has more frends. Fg. 17 llustrates the download tme versus the rank of the PlanetLab node. Agan, we observe that our dstrbuton replcaton acheves the lowest download tmes for almost all the PlanetLab nodes. We also observe that popularty-based replcaton acheves better dstrbuton performance than the no-gae scheme. The expermental results ndcate that by only allocatng lmted storage and outbound bandwdth (e.g., thefree-ter GAE platform n our experments) at the dstrbuton platform, wdely-propagatng socal contents can be well served to global users. 7. CONCLUDING REMARKS Large onlne socal networks are provdng Open APIs for developers to mplement dfferent socal applcatons. It s promsng for small socal meda companes to obtan user relatonshps and socal actons wthout buldng a new socal network. In ths paper, we explore an effcent and economcal cloud-based socal applcaton deployment after a socal meda company has developed ther applcaton. Wth measurement studes, we show that even f the socal applcaton 311

12 s attractng users globally, the propagaton can be qute localzed; and even f the propagaton s hghly dynamcal for users and contents, the regonal propagaton patterns can be hghly predctable. A local processng and global dstrbuton desgn prncple can be effectvely used n cloudbased socal applcaton deployment. We developed a theoretcal framework to desgn our algorthms for computaton nstance allocaton and content replcaton, whch are mplemented n our prototype of an example socal applcaton on Amazon EC2 and Google AppEngne. The superorty of our desgn s confrmed by trace-drven experments. Acknowledgment Ths work has been partally supported by the Natonal Basc Research Program of Chna (973) under Grant No. 211CB3226, the Natonal Natural Scence Foundaton of Chna under Grant No / , the Natonal Sgnfcant Scence and Technology Projects of Chna under Grant No. 212ZX1391-3, and the research fund of Tsnghua-Tencent Jont Laboratory for Internet Innovaton Technology. 8. REFERENCES [1] [2] [3] [4] [5] H. Akake. Fttng Autoregressve Models for Predcton. Annals of the Insttute of Statstcal Mathematcs, 21(1): , [6] M. Armbrust, A. Fox, R. Grffth, A. Joseph, R. Katz, A. Konwnsk, G. Lee, D. Patterson, A. Rabkn, I. Stoca, et al. A Vew of Cloud Computng. Communcatons of the ACM, 53(4):5 58, 21. [7] F. Benevenuto, T. Rodrgues, M. Cha, and V. Almeda. Characterzng User Behavor n Onlne Socal Networks. In Proc. of ACM IMC, 29. [8] M.Cha,A.Mslove,andK.Gummad.A measurement-drven analyss of nformaton propagaton n the flckr socal network. In Proc. of ACM WWW, 29. [9] X. Cheng and J. Lu. Load-Balanced Mgraton of Socal Meda to Content Clouds. In Proc. of NOSSDAV, 211. [1] N. Chohan, C. Bunch, S. Pang, C. Krntz, N. Mostafa, S. Soman, and R. Wolsk. AppScale: Scalable and Open AppEngne Applcaton Development and Deployment. Cloud Computng, 34(2):57 7, 21. [11] N. Ellson et al. Socal Network Stes: Defnton, Hstory, and Scholarshp. Journal of Computer-Medated Communcaton, 13(1):21 23, 27. [12] B. Furht and A. Escalante. Handbook of Cloud Computng. Sprnger-Verlag New York Inc, 21. [13] B. B. V. Gnedenko and I. Kovalenko. Introducton to Queueng Theory. Brkhauser, [14] L. Guo, E. Tan, S. Chen, X. Zhang, and Y. Zhao. Analyzng Patterns of User Content Generaton n Onlne Socal Networks. In Proc. of ACM SIGKDD, 29. [15] P. Hofmann and D. Woods. Cloud Computng: the Lmts of Publc Clouds for Busness Applcatons. Internet Computng, 14(6):9 93, 21. [16] Y. Huang, T. Fu, D. Chu, J. Lu, and C. Huang. Challenges, Desgn and Analyss of a Large-Scale P2P-VoD System. In Proc. of ACM SIGCOMM, 28. [17] B. Huffaker, M. Fomenkov, D. Plummer, D. Moore, and K. Claffy. Dstance Metrcs n the Internet. In Proc. of IEEE Internatonal Telecommuncatons Symposum (ITS), 22. [18] A. Kaplan and M. Haenlen. Users of the World, Unte! the Challenges and Opportuntes of Socal Meda. Busness horzons, 53(1):59 68, 21. [19] I. Konstas, V. Stathopoulos, and J. Jose. On Socal Networks and Collaboratve Recommendaton. In Proc. of ACM SIGIR, 29. [2] H. Kwak, C. Lee, H. Park, and S. Moon. What Is Twtter, a Socal Network or a News Meda? In Proc. of ACM WWW, 21. [21] A. L, X. Yang, S. Kandula, and M. Zhang. CloudCmp: Comparng Publc Cloud Provders. In Proc. of ACM IMC, 21. [22] M. Mller. Cloud Computng: Web-Based Applcatons That Change the Way You Work and Collaborate Onlne. Que, 28. [23] T. Mlls. Tme Seres Technques for Economsts. Cambrdge Unv Pr, [24] J. Pujol, V. Erramll, G. Sganos, X. Yang, N. Laoutars, P. Chhabra, and P. Rodrguez. The Lttle Engne(s) That Could: Scalng Onlne Socal Networks. ACM SIGCOMM Computer Communcaton Revew, 4(4): , 21. [25] Z. Rehman, F. Hussan, and O. Hussan. Towards Mult-Crtera Cloud Servce Selecton. In Proc. of IEEE Internatonal Conference on Innovatve Moble and Internet Servces n Ubqutous Computng, 211. [26] B. Rmal, E. Cho, and I. Lumb. A Taxonomy and Survey of Cloud Computng Systems. In IEEE Internatonal Jont Conference on INC, IMS and IDC, 29. [27] S. Scellato, C. Mascolo, M. Musoles, and J. Crowcroft. Track Globally, Delver Locally: Improvng Content Delvery Networks by Trackng Geographc Socal Cascades. In Proc. of ACM WWW, 211. [28] D. A. Tran, K. Nguyen, and C. Pham. S-CLONE: Socally-Aware Data Replcaton for Socal Networks. Computer Networks, 56(7):21 213, 212. [29] Z. Wang, L. Sun, X. Chen, W. Zhu, J. Lu, M. Chen, and S. Yang. Propagaton-based Socal-aware Replcaton for Socal Vdeo Contents. In Proc. of ACM Multmeda, 212. [3] Z. Wang, L. Sun, C. Wu, and S. Yang. Gudng Internet-Scale Vdeo Servce Deployment Usng Mcroblog-Based Predcton. In Proc. of IEEE INFOCOM Mn-Conference, 212. [31] Y.Wu,C.Wu,B.L,L.Zhang,Z.L,andF.C.Lau. Scalng Socal Meda Applcatons Into Geo-Dstrbuted Clouds. In Proc. of IEEE INFOCOM,